Engineering Mechanics

Proceedings Vol. 29 (2023)

ENGINEERING MECHANICS 2023

Copyright © 2023 Institute of Thermomechanics of the Czech Academy of Sciences, Prague

 

Šnajder J., Krejsa J.
pages 251 - 254, full text

The paper presents the framework MediaPipe as a tool to extract pose features for the task of word-level isolated sign language recognition. It tests the framework’s suitability on the state-of-the-art sign language dataset AUTSL. Extracted sequences of pose features are classified by the Long Short-Term Memory recurrent neural network constructed with the TensorFlow computational library. The paper describes the proposed method flow, preprocessing of the extracted features, and training. Obtained results are then validated on test datasets, and the impact of face landmarks is evaluated. The top-1 accuracy with face landmarks is 49.89 %, while 53.21 % without them.

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